A bio-signal refers to any infoimative time-series signal in living beings and is usually continually measured in electrical voltage levels. Some well-known medical applications of bio-signals include Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyogram (EMG), Electrooculography (EOG) and Photoplethysmogram (PPG). In clinical practice, cardiologists are able to make diagnoses in heart diseases using ECG. Some helpful features to discriminate the cardiac abnormalities include the presence, duration and the location of the PQRST waves.
FIG. 1A shows the ECG of a healthy normal heart. It is noted that three deflections, P-QRS-T complexes, follow in this order and are easily differentiable. The beat rhythm is paced between 60 and 100 per minute at rest. In contrast, atrial fibrillation (AF) is one of the most common heart diseases and is characterized by the irregular fluctuation in the ECG baseline. Although the ECG baseline fluctuation is rapid and irregular, the QRS complex is usually normal. FIG. 1B illustrates this disease. Atrial flutter (AFL) is another example of abnormal heart rhythm activities. This disease is often characterized by the disappearance of the interval between the end of T-wave and beginning of P-wave. The flutter wave frequency is between 220 and 300 beats per minute and the heart beat rate is usually over 100 per minute. FIG. 1C depicts this type of heart rhythm behavior.
In practice, electroencephalogram can provide support for and help the epilepsy diagnosis and underlying epilepsy syndrome classification. There are four main types of waves in EEG: alpha, beta, theta and delta. These four waves are shown in FIG. 2A. For a normal awake person, the EEG consists of mainly alpha and some beta activities. In epileptiform activity, sharp and spike waves are observed. FIG. 2B and FIG. 2C illustrate the EEG waves in normal condition and in epileptiform activity, respectively.
Specifying aforementioned abnormalities involves ingenious heuristics and domain expertise. Unfortunately, even an expert cannot comprehensively enumerate all fundamental features (or representation) of all abnormalities. Thus, the model-based approach, which attempts to encode all knowledge in a model, cannot work effectively. In contrast to the model-based approach, the data-driven approach learns fundamental features from a large volume of data. Unfortunately, developing a good bio-signal analyzer or disease-diagnosis classifier requires a substantial amount of labeled training data. It is both laborious and expensive to obtain many labeled medical examples of any given tasks in medical analysis. For instance, a typical labeled ECG dataset is in the order of hundreds, far from the desired volume of millions or even tens of millions. Under such constraint, even the data-driven approach may fail to learn succinct feature representations.